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Airbnb·Software Engineer·Onsite - System Design / Architecture·Intermediate

Intermediate
Apr 2026

Summary

Airbnb software engineer interview with a classic game design problem that goes deeper than it looks. The coding part is straightforward but the follow-up discussion on scaling and AI is where they actually seem to care.

Questions Asked (1)

Q1

Design and implement a Connect Four game, including the board structure, player move logic, and winner detection. Then walk through time and space complexity, and discuss how you'd extend it to support an AI opponent or a scalable multiplayer service.

System DesignAlgorithms & Data StructuresTechnical Trade-offs
Author's notes

The board and move logic came out fine, grid with a column-stack approach, nothing too tricky.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then design a clean object-oriented model for the board and game logic. Implement move logic and winner detection efficiently, analyze time and space complexity, and finally discuss extensions like AI and scalable multiplayer with trade-offs.

Pro tip: Demonstrate awareness of production concerns by mentioning how you'd test the winner detection and handle edge cases like a full board or invalid moves. Also, when discussing AI, briefly compare minimax with alpha-beta pruning versus a simpler heuristic to show you understand trade-offs.

1. Clarify Requirements and Constraints

Ask about board size, win condition, input format, and whether it's for a single machine or distributed. Confirm assumptions like standard 7x6 board and four-in-a-row win.

2. Design Data Structures and Core Logic

Propose a Board class with a 2D array or list of columns, and methods for dropping a piece, checking valid moves, and detecting a win. Explain how to track the last move to optimize winner detection.

3. Analyze Complexity and Optimize

State that move logic is O(1) if using column heights, and winner detection is O(1) by checking only lines through the last move. Space is O(rows*cols). Mention potential optimizations like bitboards.

4. Discuss Extensions: AI Opponent

Outline how to add an AI using minimax with alpha-beta pruning, heuristic evaluation, and iterative deepening. Mention time complexity and how to limit search depth for responsiveness.

5. Discuss Extensions: Scalable Multiplayer Service

Describe a client-server architecture with WebSockets for real-time play, a game service for matchmaking and state management, and a database for persistence. Discuss scaling via sharding, caching, and handling concurrency.

Key Points to Mention

  • Object-oriented design with separation of concerns (Board, Player, Game).
  • Efficient winner detection by checking only lines through the last move.
  • Time and space complexity analysis for core operations.
  • AI opponent using minimax with alpha-beta pruning and heuristic evaluation.
  • Scalable multiplayer using WebSockets, matchmaking service, and state persistence.
  • Trade-offs between simplicity and scalability, and testing strategies.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.